Merge branch 'main' into patch-1

Author: sanjay-kv

blog/azure-synapse-analytics/img/azure-synapse-cover.png

@@ -61,7 +61,7 @@ The architecture divides your data journey into three layers:
 
 Each layer has one job. Each layer makes the data a little more trustworthy. By the time data reaches the end, it's reliable, consistent, and ready to power real business decisions.
 
-<!-- ![Three-layer Medallion Architecture flow diagram](./img/medallion-architecture-flow.png) -->
+![Three-layer Medallion Architecture flow diagram](./Img/medallion-architecture-flow.png)
 
 
 ## 🥉 Bronze: The "Keep Everything" Layer
@@ -121,6 +121,7 @@ Most of the heavy lifting in a data pipeline lives here. It's not glamorous work
 ### What Silver looks like in practice
 
 Here's a simple PySpark transformation from Bronze to Silver:
+- [Reference code](https://oneuptime.com/blog/post/2026-02-17-how-to-build-a-data-lakehouse-architecture-on-gcp-using-cloud-storage-dataproc-and-bigquery/view)
 
 ```python
 from pyspark.sql import SparkSession
@@ -276,7 +277,7 @@ In practice, teams often extend the model:
 - **Feature layer** — prepared datasets for ML model training, maintained by data science teams on top of Silver
 - **Semantic layer** — business-friendly models sitting between Gold and end users for self-serve BI
 
-<!--![Extended Medallion Architecture with optional Landing, Feature, and Semantic layers](./img/medallion-extended-layers.png) -->
+![Extended Medallion Architecture with optional Landing, Feature, and Semantic layers](./Img/medallion-extended-layers.png)
 
 The three-tier model is a starting point, not a ceiling. The right number of layers is whatever your team actually needs.
 
@@ -330,6 +331,7 @@ This is the exact structure we adopted after the revenue incident. Bronze preser
 - [RecodeHive - Lakehouse vs Data Warehouse](https://www.recodehive.com/blog/lakehouse-vs-warehouse)
 - [RecodeHive - Microsoft Fabric: One Platform, One Lake](https://www.recodehive.com/blog/microsoft-fabric-explained)
 - [RecodeHive - Azure Storage & ADLS Gen2](https://www.recodehive.com/blog/azure-storage-options)
+- [OneUptime - Build a Data Lakehouse on GCP](https://oneuptime.com/blog/post/2026-02-17-how-to-build-a-data-lakehouse-architecture-on-gcp-using-cloud-storage-dataproc-and-bigquery/view)
 
 ## About the Author
 

blog/azure-synapse-analytics/img/synapse-architecture.png

@@ -151,8 +151,10 @@ const config: Config = {
               value: `<div class="grid grid-cols-3 gap-4">
                  <a class="border-r col-span-1" href="/interview-prep/" target="_self">Interview Prep</a>
                  <div class="grid grid-cols-1 col-span-2">
-                   <a href="/interview-prep/" target="_self" class="nav__icons"> 🧩Technical </a>
-                   <a href="/interview-prep/" target="_self" class="nav__icons"> 💡Behavioral </a>
+                   <a href="/interview-prep/?tab=technical" target="_self" class="nav__icons"> 🧩Technical </a>
+                   <a href="/interview-prep/?tab=behavioral" target="_self" class="nav__icons"> 💡Behavioral </a>
+                   <a href="/interview-prep/?tab=companies" target="_self" class="nav__icons"> 🏢Companies </a>
+                   <a href="/interview-prep/?tab=practice" target="_self" class="nav__icons"> 🎯Practice </a>
                  </div>
                </div>`,
             },

blog/azure-synapse-analytics/img/synapse-vs-fabric-decision.png

@@ -66,7 +66,7 @@
     "rehype-katex": "^7.0.1",
     "remark-math": "^6.0.0",
     "styled-components": "^6.4.1",
-    "tailwind-merge": "^3.2.0",
+    "tailwind-merge": "^3.6.0",
     "tw-animate-css": "^1.2.8",
     "vanilla-tilt": "^1.8.1"
   },
@@ -78,13 +78,13 @@
     "@types/canvas-confetti": "^1.9.0",
     "@types/react": "^19.1.9",
     "@types/react-dom": "^19.1.7",
-    "@typescript-eslint/eslint-plugin": "^8.59.2",
-    "@typescript-eslint/parser": "^8.59.1",
+    "@typescript-eslint/eslint-plugin": "^8.59.3",
+    "@typescript-eslint/parser": "^8.59.3",
     "autoprefixer": "^10.5.0",
     "eslint": "^9.38.0",
     "eslint-plugin-react": "^7.37.5",
     "husky": "^9.1.7",
-    "lint-staged": "^17.0.3",
+    "lint-staged": "^17.0.4",
     "postcss": "^8.5.14",
     "prettier": "^3.8.3",
     "prettier-plugin-tailwindcss": "^0.6.14",

blog/azure-synapse-analytics/index.md

@@ -177,6 +177,15 @@ const blogs: Blog[] = [
     category: "data engineering",
     tags: ["Streaming Pipelines", "Real-Time Data Processing", "Data Consistency", "Data Reliability", "Resource Consumption", "Complexity", "Data Engineering"],
 
+    id: 15,
+    title: "Azure Synapse Analytics: When to Use It (And When to Choose Fabric Instead)",
+    image: "/img/blogs/azure-synapse-cover.png",
+    description:
+      "Azure Synapse Analytics is a unified analytics service that combines big data and data warehousing capabilities. This article explores when to use Azure Synapse Analytics and when to choose Fabric instead.",
+    slug: "azure-synapse-analytics",
+    authors: ["Aditya-Singh-Rathore"],
+    category: "data engineering",
+    tags: ["Azure", "Synapse Analytics", "Data Warehousing", "Big Data", "Unified Analytics", "Fabric", "Data Engineering"],
   },
 
 ];

blog/medallion-architecture/index.md

@@ -1,5 +1,5 @@
 import React from "react";
-import { useState } from "react";
+import { useState, useEffect } from "react";
 import Layout from "@theme/Layout";
 import Head from "@docusaurus/Head";
 import { motion } from "framer-motion";
@@ -39,6 +39,23 @@ const InterviewPrepPage: React.FC = () => {
   const [expandedCategories, setExpandedCategories] = useState<{
     [key: string]: boolean;
   }>({});
+
+  // Handle URL-based tab navigation
+  useEffect(() => {
+    if (typeof window !== "undefined") {
+      const params = new URLSearchParams(window.location.search);
+      const tabParam = params.get("tab");
+      if (
+        tabParam &&
+        ["overview", "technical", "behavioral", "companies", "practice"].includes(
+          tabParam
+        )
+      ) {
+        setActiveTab(tabParam as TabType);
+      }
+    }
+  }, []);
+
   const toggleCategory = (categoryIndex: number) => {
     setExpandedCategories((prev) => ({
       ...prev,